class_docstr: An object detector model which can be trained using Model Maker's training API.
  
  Attributes:
    loss_trackers: List of tf.keras.metrics.Mean objects used to track the loss
      during training.
docstr: Overrides call from tf.keras.Model.
docstr_tok: ["Overrides","call","from","tf.keras.Model","."]
code: def call(
      self,
      images: Union[tf.Tensor, Sequence[tf.Tensor]],
      image_shape: Optional[tf.Tensor] = None,
      anchor_boxes: Optional[Mapping[str, tf.Tensor]] = None,
      output_intermediate_features: bool = False,
      training: bool = None,
  ) -> Mapping[str, tf.Tensor]:
      """Overrides call from tf.keras.Model."""
      return self._model(
          images,
          image_shape,
          anchor_boxes,
          output_intermediate_features,
          training,
      )
  
imports: import os
  from typing import Mapping, Optional, Sequence, Union
  import tensorflow
  from mediapipe.model_maker.python.vision.object_detector import detection
  from mediapipe.model_maker.python.vision.object_detector import model_options
  from mediapipe.model_maker.python.vision.object_detector import model_spec
  from official.core import config_definitions
  from official.projects.qat.vision.configs import common
  from official.projects.qat.vision.modeling import factory
  from official.vision import configs
  from official.vision.losses import focal_loss
  from official.vision.losses import loss_utils
  from official.vision.modeling import factory
  from official.vision.modeling import retinanet_model
  from official.vision.modeling.layers import detection_generator
label_desc: Use image node_id 8 for calling the ObjectDetectorModel obj's underlying member method code with example usage: obj.call(images, image_shape, anchor_boxes, output_intermediate_features, training) and returns: self